Overview

Dataset statistics

Number of variables12
Number of observations449578
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory115.3 MiB
Average record size in memory269.0 B

Variable types

Text1
DateTime1
Numeric9
Categorical1

Alerts

open has 180376 (40.1%) zerosZeros
high has 180376 (40.1%) zerosZeros
low has 180376 (40.1%) zerosZeros
close has 180376 (40.1%) zerosZeros
volume has 180129 (40.1%) zerosZeros
open_interest has 108423 (24.1%) zerosZeros
turnover has 180129 (40.1%) zerosZeros

Reproduction

Analysis started2024-03-21 02:20:11.693045
Analysis finished2024-03-21 02:20:40.570682
Duration28.88 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

symbol
Text

Distinct1790
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
2024-03-21T10:20:41.775886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2247890
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCF101
2nd rowCF103
3rd rowCF105
4th rowCF107
5th rowCF109
ValueCountFrequency (%)
sr209 613
 
0.1%
sr301 610
 
0.1%
sr211 610
 
0.1%
sr309 608
 
0.1%
sr401 606
 
0.1%
sr311 606
 
0.1%
sr305 604
 
0.1%
sr207 603
 
0.1%
sr303 602
 
0.1%
sr307 599
 
0.1%
Other values (1780) 443517
98.7%
2024-03-21T10:20:43.243061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 413616
18.4%
1 255816
 
11.4%
R 118169
 
5.3%
3 117802
 
5.2%
S 108128
 
4.8%
2 106662
 
4.7%
M 99825
 
4.4%
9 89431
 
4.0%
F 89327
 
4.0%
A 87958
 
3.9%
Other values (21) 761156
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2247890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 413616
18.4%
1 255816
 
11.4%
R 118169
 
5.3%
3 117802
 
5.2%
S 108128
 
4.8%
2 106662
 
4.7%
M 99825
 
4.4%
9 89431
 
4.0%
F 89327
 
4.0%
A 87958
 
3.9%
Other values (21) 761156
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2247890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 413616
18.4%
1 255816
 
11.4%
R 118169
 
5.3%
3 117802
 
5.2%
S 108128
 
4.8%
2 106662
 
4.7%
M 99825
 
4.4%
9 89431
 
4.0%
F 89327
 
4.0%
A 87958
 
3.9%
Other values (21) 761156
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2247890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 413616
18.4%
1 255816
 
11.4%
R 118169
 
5.3%
3 117802
 
5.2%
S 108128
 
4.8%
2 106662
 
4.7%
M 99825
 
4.4%
9 89431
 
4.0%
F 89327
 
4.0%
A 87958
 
3.9%
Other values (21) 761156
33.9%

date
Date

Distinct3203
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum2011-01-04 00:00:00
Maximum2024-03-11 00:00:00
2024-03-21T10:20:43.690475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:43.893726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

ZEROS 

Distinct17266
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3434.5239
Minimum0
Maximum34680
Zeros180376
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:44.075475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1924
Q35750
95-th percentile12760
Maximum34680
Range34680
Interquartile range (IQR)5750

Descriptive statistics

Standard deviation4638.7272
Coefficient of variation (CV)1.3506172
Kurtosis4.8888053
Mean3434.5239
Median Absolute Deviation (MAD)1924
Skewness1.9921614
Sum1.5440864 × 109
Variance21517790
MonotonicityNot monotonic
2024-03-21T10:20:44.266355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180376
40.1%
2500 219
 
< 0.1%
2600 198
 
< 0.1%
2650 194
 
< 0.1%
2700 186
 
< 0.1%
2400 178
 
< 0.1%
2450 170
 
< 0.1%
2630 167
 
< 0.1%
2300 167
 
< 0.1%
2430 160
 
< 0.1%
Other values (17256) 267563
59.5%
ValueCountFrequency (%)
0 180376
40.1%
262.2 1
 
< 0.1%
280 1
 
< 0.1%
281.4 1
 
< 0.1%
281.8 1
 
< 0.1%
282.4 1
 
< 0.1%
282.6 1
 
< 0.1%
283 1
 
< 0.1%
283.4 1
 
< 0.1%
284 1
 
< 0.1%
ValueCountFrequency (%)
34680 1
< 0.1%
34500 1
< 0.1%
34460 1
< 0.1%
34450 1
< 0.1%
34300 1
< 0.1%
34200 1
< 0.1%
34050 1
< 0.1%
34000 1
< 0.1%
33995 2
< 0.1%
33875 1
< 0.1%

high
Real number (ℝ)

ZEROS 

Distinct17557
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3461.7976
Minimum0
Maximum34870
Zeros180376
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:44.489239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1947
Q35788
95-th percentile12890
Maximum34870
Range34870
Interquartile range (IQR)5788

Descriptive statistics

Standard deviation4673.902
Coefficient of variation (CV)1.3501373
Kurtosis4.890554
Mean3461.7976
Median Absolute Deviation (MAD)1947
Skewness1.9913691
Sum1.5563481 × 109
Variance21845360
MonotonicityNot monotonic
2024-03-21T10:20:44.712069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180376
40.1%
2650 184
 
< 0.1%
2500 178
 
< 0.1%
2600 161
 
< 0.1%
2700 159
 
< 0.1%
2520 156
 
< 0.1%
2400 151
 
< 0.1%
2450 146
 
< 0.1%
2630 144
 
< 0.1%
2350 144
 
< 0.1%
Other values (17547) 267779
59.6%
ValueCountFrequency (%)
0 180376
40.1%
277.4 1
 
< 0.1%
282.2 1
 
< 0.1%
284 1
 
< 0.1%
284.6 1
 
< 0.1%
286 2
 
< 0.1%
286.6 1
 
< 0.1%
287.2 1
 
< 0.1%
287.8 2
 
< 0.1%
288 1
 
< 0.1%
ValueCountFrequency (%)
34870 1
< 0.1%
34680 1
< 0.1%
34580 1
< 0.1%
34500 1
< 0.1%
34480 1
< 0.1%
34460 1
< 0.1%
34390 1
< 0.1%
34065 1
< 0.1%
34000 2
< 0.1%
33995 2
< 0.1%

low
Real number (ℝ)

ZEROS 

Distinct17187
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3406.9122
Minimum0
Maximum34180
Zeros180376
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:44.941118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1903
Q35712
95-th percentile12650
Maximum34180
Range34180
Interquartile range (IQR)5712

Descriptive statistics

Standard deviation4601.9932
Coefficient of variation (CV)1.3507812
Kurtosis4.8787692
Mean3406.9122
Median Absolute Deviation (MAD)1903
Skewness1.9917067
Sum1.5316728 × 109
Variance21178341
MonotonicityNot monotonic
2024-03-21T10:20:45.141352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180376
40.1%
2400 194
 
< 0.1%
2500 186
 
< 0.1%
2650 177
 
< 0.1%
2600 175
 
< 0.1%
2700 175
 
< 0.1%
2280 163
 
< 0.1%
2300 162
 
< 0.1%
2580 161
 
< 0.1%
2680 154
 
< 0.1%
Other values (17177) 267655
59.5%
ValueCountFrequency (%)
0 180376
40.1%
255.8 1
 
< 0.1%
270.2 1
 
< 0.1%
275.8 1
 
< 0.1%
277.2 1
 
< 0.1%
278.4 1
 
< 0.1%
280.6 1
 
< 0.1%
281 1
 
< 0.1%
281.4 1
 
< 0.1%
281.6 1
 
< 0.1%
ValueCountFrequency (%)
34180 1
< 0.1%
34070 1
< 0.1%
34005 1
< 0.1%
33695 1
< 0.1%
33480 1
< 0.1%
33475 1
< 0.1%
33460 1
< 0.1%
33420 1
< 0.1%
33370 1
< 0.1%
33350 1
< 0.1%

close
Real number (ℝ)

ZEROS 

Distinct17503
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3434.0247
Minimum0
Maximum34250
Zeros180376
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:45.330667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1924
Q35750
95-th percentile12760
Maximum34250
Range34250
Interquartile range (IQR)5750

Descriptive statistics

Standard deviation4637.4919
Coefficient of variation (CV)1.3504539
Kurtosis4.8833659
Mean3434.0247
Median Absolute Deviation (MAD)1924
Skewness1.9914675
Sum1.543862 × 109
Variance21506331
MonotonicityNot monotonic
2024-03-21T10:20:45.533790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180376
40.1%
2700 170
 
< 0.1%
2600 165
 
< 0.1%
2500 163
 
< 0.1%
2650 151
 
< 0.1%
2630 149
 
< 0.1%
2480 145
 
< 0.1%
2520 143
 
< 0.1%
2590 142
 
< 0.1%
2550 138
 
< 0.1%
Other values (17493) 267836
59.6%
ValueCountFrequency (%)
0 180376
40.1%
277.4 1
 
< 0.1%
280.6 1
 
< 0.1%
282.2 1
 
< 0.1%
283.2 3
 
< 0.1%
284.4 2
 
< 0.1%
284.6 2
 
< 0.1%
285 1
 
< 0.1%
285.4 1
 
< 0.1%
286 2
 
< 0.1%
ValueCountFrequency (%)
34250 1
< 0.1%
34245 1
< 0.1%
34090 1
< 0.1%
33910 1
< 0.1%
33795 1
< 0.1%
33790 1
< 0.1%
33725 1
< 0.1%
33690 1
< 0.1%
33600 1
< 0.1%
33585 1
< 0.1%

volume
Real number (ℝ)

ZEROS 

Distinct77359
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50168.462
Minimum0
Maximum9020298
Zeros180129
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:45.738049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q32756
95-th percentile229165
Maximum9020298
Range9020298
Interquartile range (IQR)2756

Descriptive statistics

Standard deviation230972.43
Coefficient of variation (CV)4.6039369
Kurtosis116.79662
Mean50168.462
Median Absolute Deviation (MAD)14
Skewness8.7137821
Sum2.2554637 × 1010
Variance5.3348264 × 1010
MonotonicityNot monotonic
2024-03-21T10:20:45.920334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180129
40.1%
2 11110
 
2.5%
4 8085
 
1.8%
6 4980
 
1.1%
8 4225
 
0.9%
1 3493
 
0.8%
10 3298
 
0.7%
12 2844
 
0.6%
14 2261
 
0.5%
16 2162
 
0.5%
Other values (77349) 226991
50.5%
ValueCountFrequency (%)
0 180129
40.1%
1 3493
 
0.8%
2 11110
 
2.5%
3 1975
 
0.4%
4 8085
 
1.8%
5 1362
 
0.3%
6 4980
 
1.1%
7 1009
 
0.2%
8 4225
 
0.9%
9 864
 
0.2%
ValueCountFrequency (%)
9020298 1
< 0.1%
8520802 1
< 0.1%
8394082 1
< 0.1%
8327044 1
< 0.1%
8152542 1
< 0.1%
7620302 1
< 0.1%
7491580 1
< 0.1%
7095116 1
< 0.1%
6740970 1
< 0.1%
6696414 1
< 0.1%

open_interest
Real number (ℝ)

ZEROS 

Distinct97323
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48193.437
Minimum0
Maximum2784649
Zeros108423
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:46.108020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median209
Q312721.5
95-th percentile289962.7
Maximum2784649
Range2784649
Interquartile range (IQR)12720.5

Descriptive statistics

Standard deviation159684.34
Coefficient of variation (CV)3.3134043
Kurtosis49.247801
Mean48193.437
Median Absolute Deviation (MAD)209
Skewness6.0681671
Sum2.1666709 × 1010
Variance2.5499089 × 1010
MonotonicityNot monotonic
2024-03-21T10:20:46.294516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108423
 
24.1%
2 20217
 
4.5%
4 11740
 
2.6%
6 6982
 
1.6%
8 5322
 
1.2%
1 5133
 
1.1%
10 4220
 
0.9%
12 3770
 
0.8%
14 2825
 
0.6%
16 2537
 
0.6%
Other values (97313) 278409
61.9%
ValueCountFrequency (%)
0 108423
24.1%
1 5133
 
1.1%
2 20217
 
4.5%
3 1942
 
0.4%
4 11740
 
2.6%
5 820
 
0.2%
6 6982
 
1.6%
7 564
 
0.1%
8 5322
 
1.2%
9 398
 
0.1%
ValueCountFrequency (%)
2784649 1
< 0.1%
2778219 1
< 0.1%
2774995 1
< 0.1%
2763784 1
< 0.1%
2738080 1
< 0.1%
2730906 1
< 0.1%
2712606 1
< 0.1%
2708888 1
< 0.1%
2705736 1
< 0.1%
2702689 1
< 0.1%

turnover
Real number (ℝ)

ZEROS 

Distinct205431
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203108.88
Minimum0
Maximum39654756
Zeros180129
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:46.496939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median54.46
Q312750.228
95-th percentile1060254.4
Maximum39654756
Range39654756
Interquartile range (IQR)12750.228

Descriptive statistics

Standard deviation915672.09
Coefficient of variation (CV)4.5082818
Kurtosis204.93758
Mean203108.88
Median Absolute Deviation (MAD)54.46
Skewness10.572507
Sum9.1313286 × 1010
Variance8.3845538 × 1011
MonotonicityNot monotonic
2024-03-21T10:20:46.704955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 180129
40.1%
5.2 44
 
< 0.1%
10.72 44
 
< 0.1%
10 44
 
< 0.1%
10.84 42
 
< 0.1%
5.66 42
 
< 0.1%
5.6 42
 
< 0.1%
5.3 42
 
< 0.1%
5.4 41
 
< 0.1%
5.36 40
 
< 0.1%
Other values (205421) 269068
59.8%
ValueCountFrequency (%)
0 180129
40.1%
1.67 1
 
< 0.1%
1.69 1
 
< 0.1%
1.7 1
 
< 0.1%
1.71 2
 
< 0.1%
1.72 1
 
< 0.1%
1.74 2
 
< 0.1%
1.78 1
 
< 0.1%
1.8 1
 
< 0.1%
1.81 1
 
< 0.1%
ValueCountFrequency (%)
39654756.47 1
< 0.1%
39305846.68 1
< 0.1%
36375274.39 1
< 0.1%
36281334.95 1
< 0.1%
35415192.82 1
< 0.1%
34006799.63 1
< 0.1%
33873889.59 1
< 0.1%
33778219.42 1
< 0.1%
33248029.06 1
< 0.1%
33086427.04 1
< 0.1%

settle
Real number (ℝ)

Distinct18291
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5531.7246
Minimum264.4
Maximum34540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:46.901477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum264.4
5-th percentile713.8
Q12384
median3224
Q36940
95-th percentile19305
Maximum34540
Range34275.6
Interquartile range (IQR)4556

Descriptive statistics

Standard deviation5228.8326
Coefficient of variation (CV)0.94524456
Kurtosis4.7261832
Mean5531.7246
Median Absolute Deviation (MAD)1958
Skewness2.1245931
Sum2.4869417 × 109
Variance27340690
MonotonicityNot monotonic
2024-03-21T10:20:47.100438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801.4 3091
 
0.7%
2479 2651
 
0.6%
2360 2473
 
0.6%
3122 2193
 
0.5%
2662 1860
 
0.4%
2973 1749
 
0.4%
3198 1204
 
0.3%
2759 591
 
0.1%
2444 486
 
0.1%
2658 434
 
0.1%
Other values (18281) 432846
96.3%
ValueCountFrequency (%)
264.4 1
< 0.1%
266 1
< 0.1%
266.2 1
< 0.1%
266.6 1
< 0.1%
268.4 1
< 0.1%
269.4 1
< 0.1%
273.2 1
< 0.1%
279 1
< 0.1%
282 1
< 0.1%
282.2 1
< 0.1%
ValueCountFrequency (%)
34540 1
< 0.1%
34360 1
< 0.1%
34270 1
< 0.1%
34130 1
< 0.1%
34065 1
< 0.1%
34040 1
< 0.1%
33960 1
< 0.1%
33815 1
< 0.1%
33695 1
< 0.1%
33650 1
< 0.1%

pre_settle
Real number (ℝ)

Distinct18282
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5531.8589
Minimum264.4
Maximum34540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-03-21T10:20:47.290711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum264.4
5-th percentile713
Q12384
median3222
Q36939
95-th percentile19310
Maximum34540
Range34275.6
Interquartile range (IQR)4555

Descriptive statistics

Standard deviation5229.7585
Coefficient of variation (CV)0.945389
Kurtosis4.7271734
Mean5531.8589
Median Absolute Deviation (MAD)1958
Skewness2.1249463
Sum2.487002 × 109
Variance27350374
MonotonicityNot monotonic
2024-03-21T10:20:47.484853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801.4 3083
 
0.7%
2479 2647
 
0.6%
2360 2470
 
0.5%
3122 2190
 
0.5%
2662 1857
 
0.4%
2973 1753
 
0.4%
3198 1202
 
0.3%
2759 594
 
0.1%
2444 489
 
0.1%
2658 435
 
0.1%
Other values (18272) 432858
96.3%
ValueCountFrequency (%)
264.4 1
< 0.1%
266 1
< 0.1%
266.2 1
< 0.1%
266.6 1
< 0.1%
268.4 1
< 0.1%
269.4 1
< 0.1%
273.2 1
< 0.1%
279 1
< 0.1%
282 1
< 0.1%
282.2 1
< 0.1%
ValueCountFrequency (%)
34540 1
< 0.1%
34360 1
< 0.1%
34270 1
< 0.1%
34130 1
< 0.1%
34065 1
< 0.1%
34040 1
< 0.1%
33960 1
< 0.1%
33815 1
< 0.1%
33695 1
< 0.1%
33650 1
< 0.1%

variety
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 MiB
TA
38436 
FG
32752 
SM
 
27894
SF
 
27894
MA
 
27070
Other values (26)
295532 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters899156
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCF
2nd rowCF
3rd rowCF
4th rowCF
5th rowCF

Common Values

ValueCountFrequency (%)
TA 38436
 
8.5%
FG 32752
 
7.3%
SM 27894
 
6.2%
SF 27894
 
6.2%
MA 27070
 
6.0%
SR 24714
 
5.5%
ZC 23730
 
5.3%
CF 19190
 
4.3%
RM 18967
 
4.2%
CY 18905
 
4.2%
Other values (21) 190026
42.3%

Length

2024-03-21T10:20:47.673074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ta 38436
 
8.5%
fg 32752
 
7.3%
sm 27894
 
6.2%
sf 27894
 
6.2%
ma 27070
 
6.0%
sr 24714
 
5.5%
zc 23730
 
5.3%
cf 19190
 
4.3%
rm 18967
 
4.2%
cy 18905
 
4.2%
Other values (21) 190026
42.3%

Most occurring characters

ValueCountFrequency (%)
R 118169
13.1%
S 108128
12.0%
M 99825
11.1%
F 89327
9.9%
A 87958
9.8%
C 74789
8.3%
T 46606
 
5.2%
P 42139
 
4.7%
G 32752
 
3.6%
I 32723
 
3.6%
Other values (11) 166740
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 118169
13.1%
S 108128
12.0%
M 99825
11.1%
F 89327
9.9%
A 87958
9.8%
C 74789
8.3%
T 46606
 
5.2%
P 42139
 
4.7%
G 32752
 
3.6%
I 32723
 
3.6%
Other values (11) 166740
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 118169
13.1%
S 108128
12.0%
M 99825
11.1%
F 89327
9.9%
A 87958
9.8%
C 74789
8.3%
T 46606
 
5.2%
P 42139
 
4.7%
G 32752
 
3.6%
I 32723
 
3.6%
Other values (11) 166740
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 118169
13.1%
S 108128
12.0%
M 99825
11.1%
F 89327
9.9%
A 87958
9.8%
C 74789
8.3%
T 46606
 
5.2%
P 42139
 
4.7%
G 32752
 
3.6%
I 32723
 
3.6%
Other values (11) 166740
18.5%

Interactions

2024-03-21T10:20:37.022308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:21.453392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:23.383113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:25.282834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:27.488087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:29.444197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:31.245741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:33.150952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:35.040915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:37.240728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:21.671849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:23.589036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:25.518016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:27.692506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:29.661348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:31.463424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:33.358230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:35.247958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:37.447975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:21.898335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:23.796974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:25.726599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:27.912543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:29.858810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:31.720369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:33.568625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:35.469597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:37.664437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:22.125028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:24.002043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:25.939215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:28.129219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:30.052231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:31.925839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:33.789039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:35.680469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:37.873368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:22.332270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:24.212873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:26.162500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:28.360030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:30.247850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:32.141490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:33.996746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:35.907018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:38.065030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:22.550769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:24.416966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:26.352312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:28.583300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:30.445963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:32.328651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:34.185678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:36.103225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:38.253681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:22.752025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:24.618929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:26.826059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:28.792788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:30.642335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:32.530645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:34.385637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:36.315656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:38.473033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:22.967423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:24.824884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:27.048578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:29.014802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:30.841052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:32.733641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:34.621829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:36.560443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:38.693660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:23.161563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:25.047224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:27.277283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:29.229023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:31.039793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:32.946738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:34.840025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:20:36.766390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-21T10:20:38.995884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:20:39.515580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevariety
0CF1012011-01-04 00:00:0028500.028750.028500.028570.08415461205.2328695.028660.0CF
1CF1032011-01-04 00:00:0028190.028335.027985.028225.033618084727.6728140.028190.0CF
2CF1052011-01-04 00:00:0028175.028250.027845.028070.01839652144257920.0328040.028125.0CF
3CF1072011-01-04 00:00:0028115.028280.027915.028125.029816004186.8628100.028200.0CF
4CF1092011-01-04 00:00:0028250.028350.027895.028145.05981862275888411871.5128125.028215.0CF
5CF1112011-01-04 00:00:0026260.026390.025880.026145.05018847924656301.5726155.026285.0CF
6ER1012011-01-04 00:00:002264.02266.02264.02266.08347618.122266.02241.0ER
7ER1032011-01-04 00:00:002306.02334.02306.02317.02028646.342317.02306.0ER
8ER1052011-01-04 00:00:002354.02372.02348.02368.0210547162649745.192363.02347.0ER
9ER1072011-01-04 00:00:002370.02392.02370.02388.02486330590.992383.02370.0ER
symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevariety
449568ZC4052024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449569ZC4062024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449570ZC4072024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449571ZC4082024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449572ZC4092024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449573ZC4102024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449574ZC4112024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449575ZC4122024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449576ZC5012024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC
449577ZC5022024-03-11 00:00:000.00.00.00.0000.0801.4801.4ZC